Python UnitTest and data-driven

Python has a decorator class DDT, through which we can reuse code, to achieve the purpose of data-driven testing, the official introduction of this class can refer to

Http://ddt.readthedocs.io/en/latest/index.html

Installation of DDT is simple, directly in the cmd command line input: Pip install DDT, complete the installation.

Let's look at a simple example:

1 ImportUnitTest2  fromPractise.myTestPractice.api_loginImport*3 ImportDDT4 5 6 @ddt. DDT7 classPRADDT (unittest. TestCase):8 9     defsetUp (self):Ten         Print("My Test start! ") One  A     defTearDown (self): -         Print("My test complete!") -  the@ddt. Data (["Admin","1qaz","OK"], -["Admin","","ERROR"], -["","1qaz","ERROR"], -["Admin","1234","ERROR"], +["Admin","1qaz","ERROR"]) - @ddt. Unpack +     defTEST_DDT (self, user, passwd, expect_value): Aresult =login.login (user, passwd) atSelf.assertequal (result, Expect_value, Msg=result)

The above shows a simple test class, the test object is a login function login, when the user, the password is correct, return OK, the user or password error returned errors. We add the adorner @ddt.ddt to the test class, add the adorner data and unpack to the test method, and put the test data as the above mode in data.

To perform the test:

As can be seen from the test results run by UnitTest, we have implemented 5 test cases that correspond to the 5 test data in data. That is

We read five test data and their expected results in a single test, and the code is much leaner.

Python UnitTest and data-driven